pd.boots {sdrt}

Select the model parameters using Fourier transformation method.

Description

‘pd.boots()’ estimates the number of lags in the model and the dimension of the time series central mean subspace.

Usage

pd.boots(y, p_list=seq(2,6,by=1), w1=0.1,  space = "mean",std = FALSE,
                                     density = "kernel", method = "FM", B=50)

Arguments

y

A univariate time series observations.

p_list

(default {2,3,4,5,6}). The candidate list of the number of lags, p.

w1

(default 0.1). The tuning parameter of the estimation.

space

(default “mean”). Specify the SDR subspace needed to be estimated.

std

(default FALSE). If TRUE, then standardizing the time series observations.

density

(default “kernel”). Density function for the estimation (“kernel” or “normal”).

method

(default “FM”). Estimation method (“FM” or “NW”).

B

(default 50). Number of block bootstrap sample.

Value

The output is a p-by-p matrix, estimated p and d.

dis_dp

The average block bootsrap distances.

p_hat

The estimator for p.

d_hat

The estimator for d.

References

Samadi S. Y. and De Alwis T. P. (2023). Fourier Method of Estimating Time Series Central Mean Subspace. https://arxiv.org/pdf/2312.02110.

Examples

data("lynx")
y <- log10(lynx)
p_list=seq(2,5,by=1)
fit.model=pd.boots(y,p_list,w1=0.1,B=10)
fit.model$dis_pd
fit.model$p_hat
fit.model$d_hat